Data processing: artificial intelligence – Knowledge processing system – Knowledge representation and reasoning technique
Reexamination Certificate
2008-04-01
2008-04-01
Hirl, Joseph P (Department: 2129)
Data processing: artificial intelligence
Knowledge processing system
Knowledge representation and reasoning technique
C706S045000, C706S012000
Reexamination Certificate
active
07353215
ABSTRACT:
Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets possesses structural characteristics, locational kernels can be utilized to provide measures of similarity among data points within the dataset. The locational kernels are then combined to generate a decision function, or kernel, that can be used to analyze the dataset. Where invariance transformations or noise is present, tangent vectors are defined to identify relationships between the invariance or noise and the data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel for recognizing patterns in the dataset.
REFERENCES:
patent: 5649068 (1997-07-01), Boser et al.
patent: 5950146 (1999-09-01), Vapnik
patent: 6128608 (2000-10-01), Barnhill
patent: 6157921 (2000-12-01), Barnhill
patent: 6267722 (2001-07-01), Anderson et al.
patent: 6427141 (2002-07-01), Barnhill
patent: 6658395 (2003-12-01), Barnhill
patent: 6714925 (2004-03-01), Barnhill et al.
patent: 6760715 (2004-07-01), Barnhill et al.
patent: 6789069 (2004-09-01), Barnhill et al.
patent: 6882990 (2005-04-01), Barnhill et al.
patent: 6944602 (2005-09-01), Cristianini
patent: 2003/0172043 (2003-09-01), Guyon et al.
Patrice Y. Simard et al., Transformation Invariance in Pattern a Rcognition-Tangent Distance and Tangent Propagation, 1998, Image Processing Services Research Lab, pp. 1-33.
Amir Ben-Dor et al., Tissue Classification with Gene Expression Profiles, 2000, RECOMB, pp. 54-64.
Mario A.T. Figueiredo, On Gaussian Radial basis Function Approximations: Interpretation, Extensions, and Learning Strategies, Sep. 2000, IEEE, pp. 618-621.
Barash, et al., “Context-Specific Bayesian Clustering for Gene Expression Data”,Proceedings of the 5thAnnual International Conference on Computational Biology, Apr. 2001, pp. 12-21.
Ben-Dor et al., “Tissue Classification with Gene Expression Profiles”,Proceedings of the 4thAnnual International Conference on Computational Molecular Biology, Apr. 2000, pp. 54-64.
Burges, C. J. C., “Geometry and Invariance in Kernel Based Methods”,Advances in Kernel Methods—Support Vector Learning, Edited by Schölkopf, Burges, and Smola, 1999, MIT Press.
Chapelle, O., et al., “Choosing Multiple Parameters for Support Vector Machines”,Machine Learning, 2002, pp. 131-159, vol. 46, No. 1.
Decoste, D., et al., M. C., “Distortion-Invariant Recognition Via Jittered Queries.”,Computer Vision and Pattern Recognition(CVPR-2000), Jun. 2000.
Decoste, D., et al., “Training Invariant Support Vector Machines”,Machine Learning, 2002, vol. 46, No. 3.
Haussler, D., “Convolutional Kernels on Discrete Structures”,Technical Report UCSC-CRL-99-10, Computer Science Department, University of California at Santa Cruz, 1999.
Hoffman et al., “DNA Visual and Analytical Data Mining”,Proceedings of the 8thConference on Visualization, Oct. 1997, pp. 437-441, (Abstract).
Lathrop et al., “Massively Parallel Symbolic Induction of Protein Structure/Function Relationship”,Proceedings of the 27thHawaii International Conference on System Sciences, Jan. 1991, vol. 1, pp. 585-594, (Abstract).
Leen, T., “From Data Distributions to Regularization in Invariant Learning”,Advances in Neural Information Processing Systems, 1995, vol. 7.
Lodhi, H., et al., “Text Classification Using String Kernels”, Technical Report 2000-79, NeuroCOLT, 2000,Advances in Neural Information Processing Systems, Edited by T. K. Leen, T. G. Dietterich, and V. Tresp, 2001, vol. 13, MIT Press.
McCallum et al., “Efficient Clustering of High-Dimensional Data Sets with Application to Reference Matching”,Proceedings of the 6thACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2000, pp. 169-178.
Niyogi, P., “Incorporation Prior Information in Machine Learning by Creating Virtual Examples”,IEEE Proceedings on Intelligent Signal Processing, Nov. 1998, pp. 2196-2209, 86(11).
Pavlidis et al., “Gene Functional Classification From heterogeneous Data”,Proceedings of the 5thAnnual International Conference on Computational Biology, Apr. 2001, pp. 249-255.
Platt, J., “Probabilities for Support Vector Machines”,Advances in Large Margin Classifiers, Edited by Smola, Bartlett, Schölkopf, and Scbuurmans, 2000, MIT Press, Cambridge, MA.
Schölkopf, B., et al., “Extracting Support Data for a Given Task”,First International Conference on knowledge Discovery&Data Mining, 1995, AAAI Press, (Abstract).
Schölkopf, B., et al., “Generalization Bounds Via, Elgenvalues of the Gram Matrix”,Technical report 99-035, NeuroColt, 1999.
Schölkopf, B., et al., “Nonlinear Component Analysis as a Kernel Eigenvalue Problems”,Neural Computation, 1998, pp. 1299-1319, vol. 10.
Schölkopf, B., et al., “Prior Knowledge in Support Vector Kernels”,Advances in Neural Information Processing Systems, 1998, pp. 640-646, vol. 10, MIT Press, Cambridge, MA.
Simard, P., et al., “Transformation Invariance in Pattern Recognition—Tangent Distance and Tangent Propagation”,Neural networks: Tricks of the Trade, 1998, Springer.
Smola, A. J., et al., “Sparse Greedy Matrix Approximation for Machine Learning”,Proceedings of the 17thInternational Conference on Machine Learning, 2000, pp. 911-918, Morgan Kaufman, San Francisco.
Tsuda. K., “Support Vector Classifier with Asymmetric Kernel Function”,Proceedings of ESANN'99, 1999, pp. 183-188.
Williams, C., et al., “Using the Nystrom Method to Speed Up Kernel Machines”,Advances in Neural Information Processing Systems, 2001, pp. 682-688, vol. 13, MIT Press.
Zien, A., et al., “Engineering Support Vector Machine Kernels That Recognize translation Initiation Sites”,Bioinformatics, 2000, 799-807, 16(9).
Bartlett Peter L.
Elisseeff Andre
Schoelkopf Bernhard
Health Discovery Corporation
Hirl Joseph P
Musick Eleanor M.
Procopio Cory Hargreaves & Savitch LLP
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